Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Development

In the world of technology startups, where agility and adaptability are crucial, the mantra “do things that don’t scale” often rings true. While industry experts like Paul Graham champion this philosophy, there is often less discussion about how to effectively apply this idea to coding and software development.

After eight months of building my AI podcast platform, I’ve developed a straightforward framework that I call the 3-Month Rule: any workaround or hack that isn’t designed to be scalable gets a trial period of three months. If it shows promise and proves its value, we’ll invest in a robust solution; if it doesn’t, it gets scrapped.

As engineers, we often focus on creating scalable architectures from day one, spending time on design patterns and distributed systems that may never be needed in the early stages of a startup. This mindset can lead to wasted effort—optimizing for nonexistent users and solving problems that may never arise. The 3-Month Rule compels me to produce straightforward, “imperfect” code that actually delivers results and provides insights into user needs.

Key Infrastructure Choices: The Benefits of Unscalable Solutions

Here are some of the unconventional hacks I’ve implemented, along with the valuable lessons they’ve taught me:

1. Centralized VM Management

I run everything—my database, web server, background jobs, and caching—on a single virtual machine for $40 a month. While this setup sacrifices redundancy and relies on manual backups, it has provided invaluable insights into my resource utilization. In two months, I’ve learned that my platform’s usage peaks at 4GB of RAM, sparing myself from unnecessary complexities like Kubernetes that I might have pursued without real data. Each crash (which has occurred twice) reveals unexpected vulnerabilities, allowing me to hone in on real issues.

2. Hardcoded Configurations

Instead of using separate configuration files or environment variables, I have hardcoded constants throughout my codebase. Changing a configuration means redeploying, but this setup has given me the ability to quickly reference and track configuration changes across git history. I’ve modified these settings only three times in three months, demonstrating that the time saved from building a configuration service far outweighs the quick redeploys.

3. Using SQLite for Production

Contrary to conventional wisdom, I’m running SQLite for my multi-user application. At just 47MB, it’s proving capable of